1. Field of the Invention
The present invention relates to a traveling lane detector used for detecting a lane on which a vehicle is traveling.
2. Description of the Related Art
Various techniques have been conventionally proposed for a traveling lane detector mounted on a vehicle such as an automobile. In these techniques, lane marks such as a white line drawn on a road are detected through image processing, and when the detection result reveals that the vehicle is out of the traveling lane, warning is issued to the driver, or the steering is controlled, for example.
Such lane mark detection generally uses a luminance difference between a lane mark and a road surface. A luminance difference between a lane mark and a road surface is clearly observed in an image taken from a vehicle, and a part that has a large luminance difference is called an edge. An edge corresponds to a boundary between the lane mark and the road surface, i.e., the outline of the lane mark. A larger luminance difference between adjacent pixels represents a larger edge strength. Parts each having a larger luminance difference than a predetermined threshold is extracted from the image by using a differential filter for detecting large luminance differences, and thereby edge parts of the lane mark can be extracted. Here, it is important to appropriately set this edge detection threshold, for the following reasons. When the edge detection threshold is set too high, edge parts of the lane mark cannot be detected. When the edge detection threshold is set too low, on the other hand, a number of parts that have nothing to do with the lane mark, such as the boundary of a shadow and dirt on the road surface, are extracted, resulting in erroneous detection. For accurate detection of a lane mark, it is desirable to set an edge detection threshold with which edge parts of the lane mark can be extracted stably while edges to be classified as noise can be excluded.
However, various types of lane marks are used depending on road surfaces, and are, for example, a white line, a yellow line and, in some areas, only raised pavement markers. Furthermore, even on the same road surface, the edge strength of a lane mark changes in accordance with the climate, time of a day, or the state of the road surface. In consideration of these circumstances, the edge detection threshold should rather be variable depending on road surfaces and situations, than fixed.
JP Published Patent Application No. H04-152406 A discloses a technique for setting an edge detection threshold value by using the average luminance value and the maximum luminance value of the entire image taken from the vehicle. With this technique, the threshold can be changed depending on the situation observed in the image; thus, more stable lane mark detection can be performed.
For example, assume that a lane with a white line as a vehicle-left-side lane mark and a yellow line as a vehicle-right-side lane mark is captured by a black-and-white camera. The luminance difference, thus also the edge strength, between a white line and a road surface is larger than that between a yellow line and a road surface, in general. Accordingly, in the above case, if only a single threshold is used, satisfactory extraction of edge parts of both the left and right lane marks is not possible because optimal edge detection thresholds for the left and right lane marks are different. Thus, it is desirable to set edge detection thresholds for the left and right lane marks individually.
JP Published Patent Application No. H06-341821 A discloses a technique for dividing an image taken from a vehicle into a left part and a right part, thereby setting an edge detection threshold and performing lane mark extraction processing and the like for each of the parts independently. With this technique, even when different types of lane marks are used on the left and right sides of a vehicle, an edge detection threshold value can be set appropriately for each of the sides. In addition, when different kinds of lane marks, for example, raised pavement markers on the left side and a white line on the right side, are used, a detection method itself as well as an edge detection threshold can be set differently for each of the sides.
However, when the left and right lane marks is detected from two simply-divided images, the following problem arises. When a vehicle crosses a lane mark while, for example, changing lanes, a lane mark 108 is sometimes positioned between two images 103 and 105 and on boundaries of the images 103 and 105, as shown in FIG. 5. In such a case, stable detection of the lane mark 108 may not be possible, because the lane mark 108 is outside the left and right images at the same time, ending up losing track of the lane mark 108. Once track of the lane mark is lost, it takes time to accurately detect the lane again, and vehicle control using results of traveling lane detection is not possible in the meantime.
In light of the problem, alternative methods, different from that using two simply-divided images, can be employed to detect lane marks. For example, trapezoidal windows 201 are used as shown in FIG. 6A, or multiple small rectangular windows 202 appearing only around lane marks are used as shown in FIG. 6B. These techniques are both capable of addressing the case in which the vehicle crosses a lane mark. However, in the techniques, the windows each move by following the position of a lane mark candidate detected first. For this reason, once a noise part, which is not a lane mark, is detected erroneously as the lane mark, the erroneous detection of the noise part may continue for a while. In addition, since windows used for detection have a more complicated shape than a rectangle, or a large number of windows are used, it is difficult to speed up processing by using parallel processing.